P08-10 Clustering of unhealthy lifestyle factors in occupational groups in the Swedish workforce

Abstract Background The physical activity pattern of the population, as well as the tasks of different occupational groups, have changed over the past decades. Hence, studies within and between different occupational groups, and not just between white and blue collar workers, are central for current risk group analyses. The aim was to study clustering of unhealthy lifestyle factors in different occupational groups in a large sample of men and women from the Swedish working population. Methods 72,855 individuals aged 18-75 years (41% women) from the Swedish working population who participated in a nationwide occupational health service screening between 2014-2019 were included in this cross-sectional descriptive study. Nine different occupational groups were identified based on the International Standard Classification of Occupation 2008. Exercise, diet, smoking habits and perceived health were self-reported. Cardiorespiratory fitness was estimated using a submaximal cycle test. Blood pressure and BMI was assessed through physical examination. Logistic regression modelling assessed OR (95%CI) for clustering of unhealthy lifestyle factors, defined as ‘3 of the following; low exercise, poor diet, daily smoking, poor perceived health, low fitness, high blood pressure and high BMI in the different occupational groups. Results The OR (95% CI) for clustering of unhealthy lifestyle factors were, compared to managers that served as reference, 1.00 (0.89-1.11) for professionals, 1.25 (1.11-1.39) for associate professionals, 1.93 (1.71-2.18) for clerical support workers, 2.40 (2.14-2.70) for service and sales workers, 1.63 (1.29-2.05) for agricultural, forestry and fishery workers, 2.23 (1.99-2.49) for craft and related trades workers, 2.52 (2.25-2.83) for plant and machine operators, and assemblers, and 2.62 (2.26-3.05) for elementary occupations. Comparing occupational groups within ‘service and sales workers’ and ‘plant and machine operators, and assemblers’, revealed significantly higher OR for professionals in care workers (OR2.92 (2.55-3.34)) and in drivers (OR 3.32(2.86-3.87)) compared to each of the main occupational groups. Conclusion There were large variations in clustering of unhealthy lifestyle-related factors between as well as within different white and blue collar occupations. This study suggest that targeted measures of health promotion are foremost needed in blue collar occupations, however with some white collar sub-occupations being at similar need as blue collar occupations.


Background
The physical activity pattern of the population, as well as the tasks of different occupational groups, have changed over the past decades. Hence, studies within and between different occupational groups, and not just between white and blue collar workers, are central for current risk group analyses. The aim was to study clustering of unhealthy lifestyle factors in different occupational groups in a large sample of men and women from the Swedish working population. Methods 72,855 individuals aged 18-75 years (41% women) from the Swedish working population who participated in a nationwide occupational health service screening between 2014-2019 were included in this cross-sectional descriptive study. Nine different occupational groups were identified based on the International Standard Classification of Occupation 2008. Exercise, diet, smoking habits and perceived health were selfreported. Cardiorespiratory fitness was estimated using a submaximal cycle test. Blood pressure and BMI was assessed through physical examination. Logistic regression modelling assessed OR (95%CI) for clustering of unhealthy lifestyle factors, defined as '3 of the following; low exercise, poor diet, daily smoking, poor perceived health, low fitness, high blood pressure and high BMI in the different occupational groups.

Conclusion
There were large variations in clustering of unhealthy lifestylerelated factors between as well as within different white and blue collar occupations. This study suggest that targeted measures of health promotion are foremost needed in blue

Background
The association between physical activity (PA) and social, economic and psychological factors in young adults is well documented. By contrast, the mechanisms by which their perceived options to be active in life shape their actual PA behavior are less well understood. By taking into account individual competences as well as social, economic and cultural resources, Sen's capability approach and Bourdieu's theory of capitals may contribute to a better understanding of how PA levels depend on the chances young adults have to realize activity in daily life. This study explores the influence of a set of PA-related capabilities, conversion factors and different forms of capitals on leisure-time PA among young Swiss adults.

Methods
We analyzed data from the Swiss Federal Survey of Adolescents (YASS), specifically the 2010/11 and 2014/15 panels, to explore capabilities for PA among young Swiss adults (N = 21894; aged 18-25 years). We applied stepwise linear regression analyses to explore the association between continuous PA scores, capabilities to be active, cultural (parental education, parental cultural objects), economic (household equivalent income) and social resources (social connections of parents), as well as individual conversion factors (self-efficiency, own education, health literacy).

Results
Preliminary findings suggest that higher capabilities to be physically active, being female, having a higher education, having parents with a higher education or a higher number of cultural objects, scoring higher on the health literacy scale and being more self-efficient have a statistically significant positive effect on leisure-time PA.

Conclusions
In line with Sen's capability approach and Bourdieu's theory of capitals, our findings indicate positive associations between leisure-time PA, the perceived capabilities to be active in life, P08-12 Equity review of the relationship between the physical environment and physical activity